Monday, March 28, 2016

A few days after I moved to Montreal to start my PhD at McGill, I met with Rowan Barrett, my co-supervisor at McGill, at one of Montreal's many shamelessly hipster third-wave cafes (Pikolo, where the coffee, to be fair, is excellent) to discuss plans and projects for my first year. A common bit of advice for first-year PhD students is to write a review or meta-analysis about some aspect of their chosen field1: it leads you to read a bunch of papers (that, frankly, you probably should be reading anyway), figure out the current state of the field, and evaluate how your proposed research fits in2. Hopefully, the review forms the basis for a thesis chapter and (ideally) a published paper.

The authors, taking a break from meta-analyzing to do some
fieldwork in the Bahamas.

We discussed a few possibilities that would fit nicely with my planned thesis and settled on an idea which Rowan had had in mind for a few years: a meta-analysis of genetic selection coefficients in natural populations. Biologists have been measuring phenotypic selection gradients and differentials since the 1980s, when Lande and Arnold developed the multiple regression methods used to quantify phenotypic selection. Influential meta-analyses of these data (e.g., Kingsolver et al. 2001, Rieseberg et al. 2002, Hereford et al. 2004, Siepielski et al. 2001 and 2013, and others) have told us a lot about how natural selection operates at the phenotypic level. At the genotypic level, however, fundamental questions remain: how strong is natural selection on allelic variation? How does selection vary through time and space? How are selection coefficients distributed? That these questions remain largely unanswered is partially due to technological limitations: biologists couldn't directly measure selection on alleles until the development of molecular genetics techniques in the 1980s3. The technological advances in DNA sequencing over the last decade have made it feasible to link genotype with fitness in natural populations of organisms. By 2013, when I was starting my PhD, a number of papers had reported estimates of selection coefficients (s) for specific genetic variants. So, we began tracking down all the estimates of s in natural populations that we could find. One detail that often gets left out of the advice to write a meta-analysis as a first-year student: actually performing a meta-analysis is a ton of work! Scanning thousands of papers for suitability, inputting hundreds of estimates of selection, and scouring bibliographies for candidate studies took much, much longer than I anticipated. But, the advice also proved to be true: I read widely4, learned a ton, and we (eventually) turned the meta-analysis into a publication, now available online at Molecular Ecology. So, what exactly did we learn? The paper has all the nitty-gritty details, but here are some of the highlights:

It's an exciting time to be studying natural selection at the genetic level! We were really surprised at the breadth of research: biologists calculate selection coefficients in many different ways, in a wide variety of organisms, using many different types of data. DNA sequencing is more accessible than ever before, and this has made it much easier to incorporate genomics into field experiments. Most of our estimates of selection came from two such studies5, and we believe that genomics-enabled field experiments of adaptation will lead to a lot of new discoveries about how evolution proceeds. But researchers estimated selection in all sorts of other ways. My favorite off-the-beaten-path example: Robinson et al. 2012, who used Bayesian epidemiological modeling to quantify selection at the PRNP gene (which influences susceptibility to prion diseases) in white-tailed deer.

Given how many different ways there are to estimate selection, we were surprised to find that selection coefficients are rarely estimated. We searched thousands of plausible papers, many of which inferred natural selection at the genetic level, but a relatively small percentage of papers (~3.5%) actually quantified selection. There are, of course, many reasons why this might be the case (and we speculate on some of them in the paper), but, whatever the cause, we encourage biologists to quantify selection more often! As our paper hopefully shows, selection coefficients can help us answer some very interesting questions, and more data will lead to better conclusions.

We found that the distribution of selection coefficients was roughly exponential, with most estimates of selection being quite weak, but a few being quite strong (there were many estimates of s > 0.5!). This distribution relates to all sorts of theoretical questions (what are the dynamics of adaptive walks to a fitness optimum? What is the extent of local adaptation? How are positive and negative selection related?) and practical issues (when trying to measure selection, what values of s are possible? In Bayesian models, what sort of priors should we put on selection coefficients?). The distribution of phenotypic selection coefficients is also roughly exponential. This similarity led to some novel theoretical work, done in collaboration with Dr. Sally Otto at UBC, about how genetic architecture may (or may not) influence how these distributions are related.

The distribution of the 3416 estimates of s included in our
quantitative analyses.

I could go on, but check out the paper (http://onlinelibrary.wiley.com/doi/10.1111/mec.13559/full) for a much more thorough discussion of these issues and many others, including thoughts on how selection varies through time, the prevalence of overdominant selection, and the best ways to report and interpret selection coefficients in manuscripts.

1. See Andrew's post on how to succeed in grad school.2. Another good reason, at least in my case: I had to write a meta-analysis as a term paper for the evolutionary ecology course I was taking.3. Before that, studies of selection at the genetic level were limited to Mendelian traits/alleles, such as melanistic alleles in the peppered moth, where genotype could be reliably inferred from phenotype.4. When was the last time you read a paper from the Russian Journal of Human Genetics?5. Anderson et al. 2014, on local adaptation in a species of mustard, and Gompert et al. 2014, on selection in Timema stick insects. Related papers:
Anderson JT, Lee C-R, Mitchell-Olds T (2014) Strong selection genome-wide enhances fitness trade-offs across environments and episodes of selection. Evolution, 68, 16-31.

Sunday, March 13, 2016

Back in the day, we didn’t have a choice. Field stations
didn’t have wifi or cell phone access. Hell, those things pretty much didn’t exist –
neither did practical laptops. We went into the field with our notebooks and
sample bags and spent all of our time collecting data, talking, taking
pictures, playing cards, and so on. Then, when we got back to
civilization, we checked our email, called home, and spent months trying to decipher
what was written in our note books. These days we check our messages between samples,
we enter the data directly into our computers (immediately backed up on
DropBox), and skype with the family on a daily basis. Progress?

This post was motivated by two experiences. First, while driving through the forest on the way back from camping in Trinidad last week, a colleague checking email on his phone received a message from a journal about a decision on his
paper. No
harm there, but it felt out of context for me, and - more to the point - it reminded me of the second experience. McGill has many field courses, and I have
taught a number of them. One course is taught at the Gault Nature Reserve on
Mt. St. Hilaire, the last remaining patch of primary forest in the St. Lawrence
lowlands. I taught this course twice with a ten year gap between – and the
experience was dramatically different.

Gault Nature Reserve of McGill University.

The first time I taught the course, we didn't have cell
phone or practical wifi. We (the profs) went for walks in the forest, played
chess, drank beer, and argued argued argued about science. In fact, Andy Gonzalez and I even turned
a never-ending argument we where having into a paper that we published in Biology and Philosophy. The students
would often hang out with us in the evenings and just BS about life and science.
The second time I taught the course, much of our “down time” was spent checking email, tweeting, texting, surfing the web, and so on. It was remarkable to look around the room and see five or six people
all sitting separately working on something on their computer or phone. Some
nights the students projected youtube “fail” videos on the screen for evening
entertainment. Connectivity in the field obviously has merits (some of those
fail videos are pretty funny); however, the point of this post is to argue that
much greater merit attends connection-free field work and classes.

When we were unplugged. "Adaptation is everywhere." "No dummy, adaptation is nowhere."

We wanted to have an asterisk behind our names with the note "each author thinks the other contributed less" - but the journal wouldn't allow it.

What is the point of being in the field? If you are
conducting research, then the goal is obviously to collected data. If you are
teaching (or taking) a course, it is unarguably to observe and experience the
natural world. Field courses allow yourself and your colleagues and students to
sit back (or lean forward) to watch how insects pollinate plants or how
cheetahs kill gazelles or how every tree in a tropical rainforest seems to be a
different species. Observations like these have historically led to many new
and novel hypotheses that have enriched our understanding of nature – think of
Darwin watching and collecting Galapagos birds. Of course, such hypotheses
can also be generated sitting in front of our computer but, even then, the best ideas are motivated and informed by previous observation made in nature.
Darwin didn’t think of natural selection until he got back but the observations
he made were critical to his subsequent insights. The "origin of all my views" was how he referred to Galapagos in his autobiography.

Happily, my 10 summers of Alaskan field work were all 100% un-connected.

OK, so I do realize you can still get these sorts of insights if
you spend the morning in nature without your phone and the afternoon surfing the
web or tweeting about your observations. However, I would like to make the case
that you can do much better if you just leave the phone (and the internet
access) at home or in your drawer.

Check out Marc Johnson's impeccable demonstration of how to take a texting break (1:28)

My first supporting argument is simply that you have more
time for observing nature if you don’t spend half of it in front of your
computer. Time matters – and we never have enough of it. Deep insights about
nature require spending extended periods of time with nature: sometimes the key
observations are rare (how often does a cheetah kill a gazelle?), sometimes
they are variable (pollinators differ from the morning to the afternoon),
sometimes you have to walk for hours just to see a decent proportion of the
tree species. Then, after it gets dark , staying off the internet allows more time for discussion with students
and colleagues (assuming you are not studying nocturnal critters). These conversations can wind in circuitous ways and touch on
many topics, and it might take two hours of debate before the eureka moment
comes or until you get so annoyed by someone’s argument that you actually
decide you need to do something serious about it. (If I hadn’t argued with
Gonzalez for hours over multiple days, we would not have felt the need to write
our Whither Adaptation? paper referred to above.)

My second argument is that you simply think differently when
you have access to the web. Without such access, two people can spend hours
arguing about something that they might find the resolution to in seconds if
they just typed it into Google. In this case, I realize that connectivity has an immediate short term benefit – you don’t “waste your time” arguing about something that
you can solve in seconds. The problem, however, is that simply looking up the
answer changes your thought process. Instead of trying to argue your
way around a topic, thinking at it from different angles, and listening the logic
of other people, you simply get out the phone and KNOW the answer. In short, connectivity kills the art of argument, and the art of argument is a critical component
of scientific discourse and insight. Indeed, every paper you write is simply an argument
for or against some hypothesis. Arguing verbally about science (or anything
else) is a great way to hone your ability to make a compelling case by marshaling arguments and corralling logic in the absence of an ability to actually PROVE what you are saying.

100% unplugged while camping for a month on this tiny island and doing stickleback field work in 1999.

In field courses, I suggest that connectivity be
eliminated while outside observing or measuring or counting or experiencing
nature. Moreover, connectivity should be eliminated for extend periods of time even
while inside. For instance, cell phones and wifi access could be off from the start
of dinner until the late evening, say 9 pm at the earliest. This after-dinner
period is the optimal time for discussion and argument.

Thankfully, our field camps in Trinidad (here just last week) are not in the range of a cell phone signal.

When doing short bouts of field work (maybe one-week or less),
I suggest that cell phones and the internet should be off the entire time. I
say “the entire time” rather than “until late evening” because it is relative
straightforward to dictate behavior in the context of a course (as above) but it
is much harder to do the same thing for ourselves. Who wants to constantly police
one’s own cell phone use – like trying to diet with a Chinese buffet in your
house? You might argue “But I need to be connected because I coordinate
this or that or because such and such deadline is looming or because …” I disagree. Every time I
visit Trinidad, I don’t check messages or the internet for the entire week. I
simply figure out what deadlines are coming and complete the task before I leave. It
is certainly true that I have, when finally checking email after a trip, often seen
messages something like “Professor Hendry. I absolutely need such or such by tomorrow or
I need you to ...[fill in an annoying administrative task here].” The funny thing is that ALL
of these crises seem to get solved without me as soon as they get my email
reply that says “In the field without internet.”

This picture depicts the scene from the movie The Matrix where the line shown below is uttered. It does not depict me or anyone I know or field work, which one reader seems to have thought (see comments). It is merely a well-known cultural reference to the merits of disconnecting occasionally from the online community, merits that I think you will agree accrued in the movie to Neo and to humanity. I can't promise similarly dramatic merits from unplugged field work.

Of course, I do recognize that connectivity might
be needed for longer periods in the field. I also realize that connectivity is necessary in
some instances, such as for safety reasons, or that it is a goal in other cases,
such as outreach activities, or that it is sometimes needed for research. But, if not, I encourage you
to unplug for just a little while. You won’t regret it.